Fraudulent transactions are unfortunately common in today's marketplace. According to its 2012 Online Fraud Report, CyberSource noted that fraud losses more than doubled in the last decade. In North America alone, fraud losses may reach as high as $4 billion a year (“2012 Online Fraud Report: Online Payment Fraud Trends, Merchant Practices and Benchmarks.” CyberSource. http://cybersource.com. Page 1). Most of these losses are absorbed by merchants involved in fraudulent transaction or a credit card issuer. As a result of the huge losses that may be incurred by financial institutions and merchants, financial institutions are fighting back with the use of sophisticated technology to detect and prevent fraudulent transactions.
Fraudulent transactions may take many forms, such as credit card theft, money laundering, securities fraud, skimming, tax havens, and other financial crimes. Federal laws and federal authorities assist financial institutions in prosecuting criminals, but financial institutions still desire to further protect their assets from fraudulent transactions and other financial crime. To protect assets and identify fraud, financial institutions and third party software companies working with financial institutions have developed fraud detection systems and applications.
Conventional fraud detection applications are generally rule-based. Rule-based fraud detection systems generate many false positives because any time that an event defined as fraud by a rule occurs, the fraud detection system generates an alert. In some circumstances, the event determined to be fraud by the rule-based system is not fraudulent, but simply a result of the rule-based system lacking all of the facts. Because rule-based fraud detection systems do not gather enough information, false positives can arise.
While rule-based fraud detection systems may generate many alerts, which may be erring on the side of caution, false positives require a fraud case worker to perform many additional investigative steps to determine if fraud has actually occurred. These additional steps may consume a lot of the case worker's time and lead to mistakes. Thus, a fraud detection system must minimize the number of false positives and find true fraudulent activity so that case worker time may be saved and more instances of real fraud may be discovered by the fraud detection application.
Also, because the rule-based fraud detection systems analyze only one transaction at a time, false positives may arise because the fraud detection system does not know the history of certain funds. For example, a bank customer may wire a very large sum of money. Such a wire transfer may be an unusual financial action for that customer. In a rule-based system such a wire transaction may trigger money laundering concerns. However, without knowing the source of the funds or other transaction details, the rule-based system cannot know for certain whether fraudulent activity has occurred.
Also, rule-based fraud detection systems may overlook some financial activity deemed to be insignificant because the rule-based fraud detection system is only looking at transactions from a narrow scope. For example, money may be laundered using small amounts, which are deemed insignificant when only looking at one transaction, such as a wire transfer. These “insignificant” financial transactions may be significant in the aggregate, or significant when viewed in the context of other transactions. So, a fraud detection system should be able to study and analyze many transactions across many different payment processing networks to find fraud that rule-based systems may miss or overlook.